A Perceptually Motivated Approach for Speech Enhancement Based on Deep Neural Network

Wei HAN  Xiongwei ZHANG  Gang MIN  Meng SUN  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E99-A   No.4   pp.835-838
Publication Date: 2016/04/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E99.A.835
Type of Manuscript: LETTER
Category: Speech and Hearing
perceptually motivated,  deep neural network,  speech enhancement,  masking residual noise,  

Full Text: PDF(508.3KB)>>
Buy this Article

In this letter, a novel perceptually motivated single channel speech enhancement approach based on Deep Neural Network (DNN) is presented. Taking into account the good masking properties of the human auditory system, a new DNN architecture is proposed to reduce the perceptual effect of the residual noise. This new DNN architecture is directly trained to learn a gain function which is used to estimate the power spectrum of clean speech and shape the spectrum of the residual noise at the same time. Experimental results demonstrate that the proposed perceptually motivated speech enhancement approach could achieve better objective speech quality when tested with TIMIT sentences corrupted by various types of noise, no matter whether the noise conditions are included in the training set or not.